Accuracy evaluations of human moving pattern using communication quality based on machine learning

Wataru Kawakami; Kenji Kanai; Bo Wei; Jiro Katto

doi:10.1109/GCCE.2017.8229351

Accuracy evaluations of human moving pattern using communication quality based on machine learning

Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

Original language	English
Title of host publication	2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-2
Number of pages	2
ISBN (Electronic)	9781509040452
DOIs	https://doi.org/10.1109/GCCE.2017.8229351
Publication status	Published - 2017 Dec 19
Event	6th IEEE Global Conference on Consumer Electronics, GCCE 2017 - Nagoya, Japan Duration: 2017 Oct 24 → 2017 Oct 27

Publication series

Name	2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
Volume	2017-January

Other

Other	6th IEEE Global Conference on Consumer Electronics, GCCE 2017
Country/Territory	Japan
City	Nagoya
Period	17/10/24 → 17/10/27

Keywords

Communication quality
Human activity recognition
Machine learning
Mobile sensing

ASJC Scopus subject areas

Media Technology
Instrumentation
Electrical and Electronic Engineering

Access to Document

10.1109/GCCE.2017.8229351

Cite this

Kawakami, W., Kanai, K., Wei, B., & Katto, J. (2017). Accuracy evaluations of human moving pattern using communication quality based on machine learning. In 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017 (pp. 1-2). (2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2017.8229351

Accuracy evaluations of human moving pattern using communication quality based on machine learning. / Kawakami, Wataru; Kanai, Kenji; Wei, Bo et al.
2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-2 (2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kawakami, W, Kanai, K, Wei, B & Katto, J 2017, Accuracy evaluations of human moving pattern using communication quality based on machine learning. in 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-2, 6th IEEE Global Conference on Consumer Electronics, GCCE 2017, Nagoya, Japan, 17/10/24. https://doi.org/10.1109/GCCE.2017.8229351

Kawakami W, Kanai K, Wei B, Katto J. Accuracy evaluations of human moving pattern using communication quality based on machine learning. In 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-2. (2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017). doi: 10.1109/GCCE.2017.8229351

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abstract = "In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.",

keywords = "Communication quality, Human activity recognition, Machine learning, Mobile sensing",

author = "Wataru Kawakami and Kenji Kanai and Bo Wei and Jiro Katto",

note = "Funding Information: This work was supported by JSPS KAKENHI Grant Numbers 15H01684 and 17K12681. Publisher Copyright: {\textcopyright} 2017 IEEE.; 6th IEEE Global Conference on Consumer Electronics, GCCE 2017 ; Conference date: 24-10-2017 Through 27-10-2017",

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AB - In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

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Accuracy evaluations of human moving pattern using communication quality based on machine learning

Abstract

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